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The Journal of Risk and Insurance publishes rigorous, original research
in insurance economics and risk management. This includes the following areas
of specialization: (1) industrial organization of insurance markets; (2) management
of risks in the private and public sectors; (3) insurance finance, financial
pricing, financial management; (4) economics of employee benefits, pension plans,
and social insurance; (5) utility theory, demand for insurance, moral hazard,
and adverse selection; (6) insurance regulation; (7) actuarial and statistical
methodology; and (8) economics of insurance institutions. Both theoretical and
empirical submissions are encouraged. Empirical work should provide tests of
hypotheses based on sound theoretical foundations.
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Abstract

This article analyzes the accuracy of the principal models used by U.S. insurance regulators to predict insolvencies in the property-liability insurance industry and compares these models with a relatively new solvency testing approach - cash flow simulation. Specifically, we compare the risk-based capital (RBC) system introduced by the National Association of Insurance Commissioners (NAIC) in 1994, the Financial Analysis and Surveillance Tracking (FAST) audit ratio system used by the NAIC, and a cash flow simulation model developed by the authors. Both the RBC and FAST systems are static, ratio-based approaches to solvency testing, whereas the cash flow simulation model implements dynamic financial analysis. Logistic regression analysis is used to test the models for a large sample of solvent and insolvent property-liability insurers, using data from the years 1990 through 1992 to predict insolvencies over three-year prediction horizons. We find that the FAST system dominates RBC as a static method for predicting insurer insolvencies. Further, we find the cash flow simulation variables add significant explanatory power to the regressions and lead to more accurate solvency prediction than the ratio-based models taken alone.